Spatialization of station measured net ecosystem exchange using artificial neural network

Net ecosystem exchange (NEE) is a critical ecological parameter indicating the exchange of carbon dioxide between vegetation and atmosphere, which is widely used in the field of carbon cycle researches. It is common measurement by flux tower based on eddy covariance technique can only represent local status, however regional NEE is much more important. This paper introduces a spatialization method of NEE based on artificial neural network (ANN). 14 input nodes are selected purposefully including meteorological variables, ecological variables, land cover variables and seasonal variables. A feed-forward back propagation neural network is trained by 92 measured samples. Validation results show that ANN is a satisfactory method for the spatialization of NEE-like ecological parameters.